Consistency in image rendering helps to allow a more accurate clinical evaluation when using x-rays and related types of diagnostic images. Images taken of the same anatomy that have the same overall dynamic range and contrast settings can be more readily compared against one another for diagnosis and for tracking various conditions, particularly for images taken of the same patient at different times and on different imaging apparatus.
However, due to differences in variables such as equipment used, techniques applied, and image pre-processing, consistent rendering of radiographic images can be difficult to achieve. Thus, even for images obtained from the same patient over a short treatment interval, there can be differences between two or more images that prevent effective comparison between them and constrain the ability of the clinician to detect subtle changes that can be highly significant. This problem relates to images whether originally obtained on film and scanned, or digitally obtained, such as using a computed radiography (CR) or digital radiography (DR) system. Some progress has been made with different types of x-rays, particularly for chest x-ray and related imaging. In practice, however, consistent image rendering has generally proved difficult to achieve.
For a number of reasons, providing consistent rendering for mammography images is acknowledged to be particularly challenging. Mammography is characterized by low power levels relative to other radiography methods and by the need to pre-process the image data that is obtained from the low-level exposure properly so that subtle changes in soft tissue can be more clearly discerned. The breast is a non-rigid 3D structure and breast compression is required for a better image quality. Both the positioning of the breast and the level of compression used can be substantially varied from one acquisition to the next. Variations in imaging techniques, compression, positioning, and image processing techniques tend to result in significant differences in image appearance and quality. Due in part to these factors and to the overall complexity of the problem, consistent rendering has not been given considerable attention for mammography imaging processing. Because of this, radiologists are often required to adapt to system-related rendering differences, even for images related to the same patient, but taken at different times.
The average breast generally has about 50% fibroglandular tissue, a mixture of fibrous connective tissue and the glandular epithelial cells that line the ducts of the breast (the parenchyma), and 50% fat tissue. However, the radiological appearance of the breast varies between individuals, in part, because of variations in the relative amounts of fatty and dense fibroglandular tissue. As a guideline for classification, the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BIRADS) has identified four major groupings for breast tissue density. Class I corresponds to breasts having high concentration of fat tissue. The Class II grouping indicates scattered fibroglandular densities. Class III indicates heterogeneously dense tissue. Class IV corresponds to extremely high breast density.
A particular problem for mammography evaluation relates to similar densities between different tissue types and similar density between cancer lesions and fibroglandular tissue, usually termed dense tissue. Women with increased mammographic parenchymal density can have four- to six times the cancer risk of women with primarily fatty breasts. Since most breast cancers develop from the epithelial cells that line the ducts of the breast, having more of this tissue, as reflected by increased mammographic density, may indicate higher likelihood of developing breast cancer. Studies have found that lesions in higher density areas are themselves more difficult to detect from the mammogram than are lesions in fatty regions, somewhat compounding the problem. The challenge on rendering of mammographic images is to properly enhance the contrast for the viewer, to better differentiate or magnify subtle differences between breast tissues and between cancers and normal breast tissues in density.
Comparison of current mammograms with prior exams has been one of the common approaches used to detect changes over time as a sign of early cancer. Because of this practice, image consistency has a role in diagnosis of breast cancer using mammography. However, image consistency rendering for mammography remains a challenge as a result of these factors.
As discussed earlier, the amount of dense tissue can vary significantly from one individual to another. While increased breast density is associated with an increased risk of breast cancer, the amount of dense tissue also decreases as age increases. Responding properly to the variation in the amount of dense tissue from one individual to another and to this variation for the same individual from one exam to another represents considerable challenge for consistency rendering in mammography, in light of the factors that govern consistency rendering in mammographic images.
Breast density, sometimes expressed as mammographic percent density, or MPD, can be calculated to help clinicians in categorizing the breast tissues into Classes I-IV described above. An approach is proposed to estimate the MPD by Huo et al. in commonly assigned U.S. Ser. No. 12/471,675 filed May 26, 2009, entitled “Assessment of Breast Density and Related Cancer Risk”. Since fat has a lower effective atomic number than that of fibroglandular tissue, there is less x-ray attenuation from fatty tissue than from denser fibroglandular tissue. Fat appears dark (that is, has a higher optical density) on a mammogram, while fibroglandular tissue appears light (that is, exhibits a lower optical density). Regions of brightness associated with fibroglandular tissue are normally considered by diagnosticians to have increased “mammographic density”. Information on breast density or MPD can help clinicians to better manage patient care and cancer risk. Use of this information in image processing could potentially help to render images consistently. Tone-scale adjustment based on the amount of dense tissue in the image as a reference will allow consistent rendering of mammographic images among patients and across different imaging modality and image processing. In addition, one can manage the rendering of dense tissue portions so that cancers can be better enhanced in dense tissue.
Computed radiography systems that use storage phosphors and digital radiography systems can offer a very wide exposure latitude (as much as 10,000:1) compared with that available from conventional screen/film systems (typically 40:1). This means that exposure error is much less serious for computed radiography at the time of image sensing and recording. However, image display apparatus have a much more limited dynamic range. Tone scale mapping in computed radiography can be specifically tailored to provide an optimal rendition of every individual image. However, most output media, such as photographic film and displays such as flat-panel or cathode ray tube (CRT) displays do not have wide enough dynamic range to display this information at nearly 10,000:1 latitude with proper visual contrast. It is, therefore, necessary to carefully allocate the available output dynamic range to display the clinically relevant part of the input code values.
Conventional methods for adjusting the intensity range and slope of radiography image values are generally not satisfactory for mammography. For general radiography, for example, methods that provide contrast improvement, such as those described in U.S. Pat. No. 5,633,511 entitled “Automatic Tone Scale Adjustment Using Image Activity Measures” to Lee et al., that constructs a tone-scale transfer curve, or disclosed in commonly assigned U.S. Pat. No. 6,778,691 entitled “Method of Automatically Determining Tone-Scale Parameters for a Digital Image” to Barski et al., generating a Look-Up Table (LUT) for obtaining a desired tone scale for an image using the slope of the tone scale curve over its mid-range densities, do not address the particular problems posed in mammography, but are better suited to more general x-ray images. Thus, for example, where mammography images for a patient taken at different times differ with respect to exposure values or other values, application of such contrast improvement techniques is not likely to provide consistent rendering that would allow more accurate assessment of condition changes by the evaluating clinician.
Contrast stretching is one method that has been proposed for providing a measure of normalization between images. For example, U.S. Pat. No. 5,357,549 entitled “Method Of Dynamic Range Compression Of An X-Ray Image And Apparatus Effectuating The Method” to Maack et al. describes a dynamic range compression technique that stretches image intensity in only a particular area of interest, such as within the lung area of a chest X-ray. In a similar approach, U.S. Pat. No. 5,835,618 entitled “Uniform And Non-Uniform Dynamic Range Remapping For Optimum Image Display” to Fang uses a method of dynamic range remapping for enhancing the image in both dark and bright intensity areas. Contrast adjusting methods such as these focus on improving the overall image appearance of individual images, which may in turn help to improve image consistency to some degree. However, these and other conventional contrast-stretching methods do not directly address inconsistency from image to image and do not address problems specifically encountered in mammography imaging.
Thus, although there have been some proposed methods for providing consistency in diagnostic image rendering, none of these methods addresses the particular problems posed by mammography. The problem of providing consistency in image appearance is complicated by a number of factors, such as by the number of different types of imaging systems that can be used, each having different preprocessing of the initial image data, by imaging techniques applied during the exam, and by viewer preferences for image content. It would be beneficial to provide solutions to the mammography rendering problem that provide consistent results for the same types of mammography images obtained at different systems, under different conditions, and at different times.